Browsing: Multimodal

The landscape of multimodal large language models (MLLMs) has shifted from experimental ‘wrappers’—where separate vision or audio encoders are stitched onto a text-based backbone—to native, end-to-end…

Google has released Gemini 3.1 Flash Live in preview for developers through the Gemini Live API in Google AI Studio. This model targets low-latency, more natural,…

def parse_click_coords(action_str): “”” Extract normalised (x, y) coordinates from a click action string. e.g., ‘click(0.45, 0.32)’ -> (0.45, 0.32) Returns None if the action is not…

Video content is now everywhere, from security surveillance and media production to social platforms and enterprise communications. However, extracting meaningful insights from large volumes of video…

Mistral AI has released Mistral Small 4, a new model in the Mistral Small family designed to consolidate several previously separate capabilities into a single deployment…

Why Document OCR Still Remains a Hard Engineering Problem? What does it take to make OCR useful for real documents instead of clean demo images? And…

This post shows you how to build a scalable multimodal video search system that enables natural language search across large video datasets using Amazon Nova models…

Google expanded its Gemini model family with the release of Gemini Embedding 2. This second-generation model succeeds the text-only gemini-embedding-001 and is designed specifically to address…

Microsoft has released Phi-4-reasoning-vision-15B, a 15 billion parameter open-weight multimodal reasoning model designed for image and text tasks that require both perception and selective reasoning. It…

How can a trillion-parameter Large Language Model achieve state-of-the-art enterprise performance while simultaneously cutting its total parameter count by 33.3% and boosting pre-training efficiency by 49%?…